Method and system for providing traffic and related information

ABSTRACT

The invention provides a system for providing traffic or related information including: a database storing historical traffic data being operable to receive substantially real time traffic data and associated data; means for integrating historical, real time and associated traffic data with respect to traveller profiles to produce customised forecasted traffic information with respect to those traveller profiles; and means for sending the customised forecasted traffic information to an intended recipient wherein the customised forecasted traffic information includes predicted travel delays for travel routes described in the traveller profiles.

FIELD OF THE INVENTION

[0001] The present invention relates to traveller information servicesand in particular to a system for providing forecasted trafficinformation to individual travellers.

BACKGROUND OF THE INVENTION

[0002] The monitoring and reporting of traffic conditions is animportant factor in the management of traffic flow. From a motoristspoint of view, it can be vital in saving commuting time and unnecessarydelays. Substantial effort has been directed to providing facilitieswhich allow a motorist or other user to access traffic and relatedinformation in a timely manner.

[0003] One type of known traffic reporting is by use of a “spotter”,namely designated persons or members of the public who report trafficincidents to radio stations or a central controller, for subsequentdissemination to the public. Such a system however, cannot sustain thedemand placed on it by today's user requirements.

[0004] More developed prior art systems include the use of sensors onroads, such as cameras that are linked to a central facility for thedissemination of traffic information. Sensors may be strategicallylocated at exits/entrances to freeways and major roads. Other systemsare cellular/mobile telephony based with sensors or designated spottersstationed on major roads and freeways. Such systems are integrated witha central control facility to provide cellular network subscribers withinformation regarding traffic flow, accidents, detours, roadconstruction, etc. Subscribers may also have the opportunity to dial inand retrieve instantaneous information regarding a particular aspect ofthe traffic network such as a freeway.

[0005] The above-described systems, however, are limited in theircapacity to provide useful customised information to subscribers. Theyare generally limited to providing the status of current trafficconditions supplemented by updated/incident reports that may give a clueto the duration of a problem. This does not satisfy the needs of themotorist who requires information relating to what the conditions willbe at some time in the future when he will be travelling past locationsthat are currently congested. Additionally, these systems do not providean indication as to whether alternate routes are available and/or thedetails of those alternate routes.

[0006] A significant drawback of prior art systems is the lack ofcustomised and critically timed information provided to individualsubscribers.

[0007] It would therefore be desirable to provide a system for reportingtraffic information to individual motorists in a timely and customisedmanner.

[0008] It would further be desirable to provide individual subscribersof a network with forecasted traffic information relevant to thoseindividual subscribers.

[0009] Any discussion of documents, acts, materials, devices, articlesor the like which has been included in the present specification issolely for the purpose of providing a context for the present invention.It is not to be taken as an admission that any or all of these mattersform part of the prior art base or were common general knowledge in thefield relevant to the present invention before the priority date of eachclaim of this application.

SUMMARY OF THE INVENTION

[0010] The present invention provides a system for providing traffic orrelated information comprising:

[0011] a database storing historical traffic data and being operable toreceive substantially real time traffic data and associated data;

[0012] means for deterministically integrating the historical and realtime traffic and associated data with respect to at least one travellerprofile to produce customised forecasted traffic information withrespect to the at least one traveller profile; and

[0013] means for sending the customised forecasted traffic informationto an intended recipient wherein the customised forecasted trafficinformation comprises at least a predicted travel delay for at least onetravel route described in the at least one traveller profile,

[0014] wherein, where there is insufficient traffic data for a link ofthe travel route, the means for integrating is operable to use availabledata in respect of a further link in place of the insufficient trafficdata on said link of said travel route in order to provide the predictedtravel delay.

[0015] In a preferred embodiment, the customised forecasted trafficinformation is transmitted to an information distributor who distributesthe traveller information to users who have subscribed to theinformation distributor for the purpose of receiving traffic informationrelevant to their travel requirements. In this instance, it is preferredthat subscribers have remote terminals in order to receive thecustomised information. The system and the terminals may provide forcommunication from the subscriber to the system. and the terminals mayprovide for communication from the subscriber to the system.

[0016] Historical and real time traffic data is likely to mostlycomprise data collected from traffic control signals and traffic sensorsand detectors placed at strategic locations throughout a trafficnetwork. The database may be operable to store historical and real timetraffic data and/or associated data for frequent retrieval. Thehistorical traffic data stored in the database preferably includes asample of previous traffic data relating to geographical areas ofinterest to subscribers. Such data may include data from strategicallyrelevant locations such as traffic congestion areas, traffic flow atparticular landmarks and speeds along specific routes. Associated datamay include data collected from other sensors and detectors such assensors for measuring and reporting temperature and rainfall and mayalso include data relating to significant events that may have an impactupon the flow of traffic through a traffic network. (eg holidays?)

[0017] Associated traffic data stored in the database preferablyincludes information on incidents, accidents, road construction,alternate routes and weather information. Associated data maybe obtainedfrom any print, electronic or radio communication which is thenconverted to data for storage in said database and subsequently used bythe system.

[0018] The means for integrating historical, real time and associatedtraffic data may include a model that provides an indication of theexpected delay for a particular link based upon historical records.Using a model as compared with referencing base data should reducestorage requirements and may also reduce computation time and henceprovide more timely results.

[0019] By providing a system according to the present invention, asubscriber is able to receive traffic or related information withrespect to his or her travelled route before and/or during the journey.The system may provide subscribers with updated and relevantly timedinformation which is forecasted with respect to a subscriber's customarytravelling patterns.

[0020] The applicant has recognised that there is a “space-time window”in which a motorist requires personalised, customised and localisedtravel information and that information received before or after that“space-time window” period is of limited use to a traveller.

[0021] Of course, the system may include a database of informationrelating to subscribers and may supply customised forecasted trafficinformation directly to subscribers.

[0022] In an embodiment of the present invention there is provided asystem for providing traffic and related information to subscriberterminals including:

[0023] a subscriber database;

[0024] a plurality of subscriber terminals in a network capable ofreceiving at least text messages;

[0025] a database storing historical traffic data being operable toreceive substantially real time traffic data and associated data;

[0026] means for integrating said historical data and said real timedata with respect to subscriber profiles stored in said subscriberdatabase, to produce customised forecasted traffic information forindividual subscribers; and

[0027] means for sending said customised forecasted traffic informationto individual subscriber terminals in said network at times that arecritical to individual subscribers wherein the customised forecastedtraffic information includes a predicted travel delay for travel routesdescribed in a subscriber's travel profile.

[0028] In a preferred form of the invention, the subscriber is amotorist and the network is a cellular or a mobile communicationsnetwork. The network may support Short Message Service (SMS), WirelessApplication Protocol (WAP) or third generation (3G) wireless broadbandnetworks.

[0029] The subscriber database preferably stores individual subscriberprofiles in a non-volatile memory with each individual subscriberprofile preferably including information regarding the identity of thesubscriber. Profiles may also include parameters such as usual traveltimes, the route usually taken and the times at which a subscriber wouldprefer to receive customised forcasted traffic information. These timesmay be considered critical by the individual subscriber. A subscribermay be provided with access to a database to alter the parameters oftheir profile. The access maybe provided via a dedicated web-site,

[0030] Subscriber terminals may include a mobile communication devicecapable of receiving traffic information and/or other relatedinformation. The device may be a mobile telephone forming part of amobile communications network and may be adapted to receive data in SMS,WAP or 3G formats. The device may even incorporate text to voice or IVRtechniques.

[0031] Preferably, the device is able to request information from thedatabase regarding historical or real time traffic information.

[0032] The means for integrating historical and real time data mayinclude a model of the expected delays for various traffic links basedupon historical data. In this instance, the model preferably includes anadaptive mathematical model for forecasting traffic information. Themodel may also compare the historical data variables with real time datain accordance with a subscribers travel route. Statistical techniquesmay be employed to determine the effects of the data variables and mayinclude multi-variate regression, multi-variate time series, spectralanalysis piece-wise daily templates or the like, or any combinationthereof. In addition to expected delays resulting from traffic density,incidents may occur that could significantly increase the historicallyexpected delays. The occurrence of an incident that may have asignificant effect upon the forecasted delay to a subscriber may be sentto a subscriber prior to his or her next major route change thusenabling the subscriber to perhaps select an alternate route in anattempt to avoid any increase to the subscriber's expected travel time.

[0033] In a particularly preferred embodiment, the system includes ameans for determining an optimal path of travel through a travelnetwork. The means for determining the optimal path of travel throughthe network may employ a method that takes account of the direction oftraffic flow on each individual travel link in the traffic network.Additionally, it is preferred that the method also take account of thedifferent delays caused by traffic signals to individual traffic flowsthrough a signal controlled intersection.

[0034] In some instances, there may only be limited historical trafficdata available for any particular traffic network or part thereof. Inaddition, real time tic signal data may only be available for a limitednumber of signal controlled intersections of the traffic network at afrequency sufficient to be relevant for the purpose of predicting traveltime. In this instance, the means for determining the optimal travelpath through a traffic network preferably implements a method ofmatching data received from the limited number of signal controlledintersections with remaining intersections in the traffic network forwhich there is no timely available traffic signal data.

[0035] Similarly, in the instance where a system is intended to be usedfor a traffic network where there is limited available traffic data, orperhaps no traffic data is available, the system may include a means fordetermining an estimate of the travel flows wherein the means implementsa method of matching signal controlled intersections from a trafficnetwork with known data to the traffic network with limited trafficdata. This enables the system to at least establish a first estimatethat may be refined over time as more traffic data for that networkbecomes available.

[0036] In a preferred embodiment, the matching of data from trafficcontrolled intersections throughout a traffic network takes account ofvarious factors including the geometry of the intersection, theorientation of the intersection and the ratio of actual flow of trafficresulting from a particular traffic signal as compared with the maximumflow of traffic possible for that same signal. This latter factor isreferred to as the “degree of saturation” (DOS). Of course, the methodof matching intersections throughout a traffic network may includeadditional factors such as historical daily averages for signal cycletimes for the intersections. The various factors used to determine amatch between intersections may be given a priority or weighting inorder to establish an order of importance for each factor. This order,or weighting, of individual factors may vary when matching intersectionswith known data of a traffic network to those of another traffic networksuch as those in other cities. In addition, the order, or weighting, ofindividual factors may vary from one region of a traffic network toanother.

[0037] The means for periodically sending customised traffic informationmay include at least one database server capable of sending at leasttext messages. The customised traffic information is preferablyforwarded to a mobile network of the subscriber. The frequency and timeof the information being formulated and dispatched may be determined bythe subscriber's travel profile. The information may be sent beforeand/or during a subscriber's travel route.

[0038] In an alternative form, the forecasted information may becustomised according to the location of the subscriber. The location orposition of the subscriber may be determined by positioning systems suchas Global Positioning System (GPS), Mobile Positioning System (MPS) orother means.

[0039] According to another aspect, the present invention provides amethod of providing traffic or related information comprising the stepsof:

[0040] a) storing historical, real time and associated traffic data in adatabase;

[0041] b) deterministically integrating said historical, real time andassociated data with respect to a traveller profile to producecustomised forecasted traffic information with respect to the travellerprofile; and

[0042] c) sending the customised forecasted traffic information to anintended recipient wherein the customised forecasted traffic informationcomprises a predicted travel delay for a travel route described in thetraveller profile;

[0043] wherein, where there is insufficient traffic data for a link ofthe travel route, the step of integrating comprises using available datain respect of a further link in place of the insufficient traffic dataon said link of said travel route in order to provide the predictedtravel delay.

[0044] In an embodiment, the present invention provides a method ofproviding traffic or related information including the steps of:

[0045] a) storing historical, real time and associated traffic data in adatabase;

[0046] b) integrating said historical, real time and associated datawith respect to traveller profiles to produce customised forecastedtraffic information with respect to those traveller profiles; and

[0047] c) sending the customised forecasted traffic information tosubscriber terminals that are capable of receiving at least textmessages in a network and at times that are critical to subscriberswherein the customised forecasted traffic information includes predictedtravel delays for travel routes described in the subscriber's profile.

[0048] In a particularly preferred embodiment, the method includes thestep of determining an optimal path of travel through a travel networkwhich preferably takes account of the direction of traffic flow on eachindividual travel link in the network. Additionally, it is preferredthat the method also take account of the different delays caused bytraffic signals to individual traffic flows through a signal controlledintersection.

[0049] In some instances, there may only be limited historical trafficdata available for any particular traffic network or part thereof.Additionally, real time traffic signal data may only be available for alimited number of signal controlled intersections of the traffic networkat a frequency sufficient for that data to be relevant for the purposeof predicting travel time. In this instance, the means for determiningthe optimal path through the traffic network preferably implements amethod of matching data received from the limited number of signalcontrolled intersections to remaining intersections in the trafficnetwork for which there is no timely available traffic signal data.

[0050] Similarly, in the instance where a method is intended to be usedfor a traffic network where there is limited available traffic data, orperhaps no traffic data is available, the method may include the step ofmatching signal controlled intersections from a traffic network withknown data to the traffic network with limited traffic data.

[0051] In a preferred embodiment, the method of matching data fromtraffic controlled intersections throughout a traffic network takesaccount of various factors including the geometry of the intersection,the orientation of the intersection and the ratio of actual flow oftraffic resulting from a particular traffic signal as compared with themaximum flow of traffic possible for that same signal (i.e. the DOS). Ofcourse, the method of matching intersections throughout a trafficnetwork may include additional factors such as historical daily averagesfor signal cycle times for the intersections. The various factors usedto determine a match between intersections may be given a priority orweighting in order to establish an order of importance of each factor.This order, or weighting, of individual factors may vary when matchingintersections with known data to those of another traffic network suchas those in other cities. In addition, the order, or weighting, ofindividual factors may vary from one region of a traffic network toanother.

BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The following description refers in more detail to the variousfeatures of the traffic information system of the present invention. Tofacilitate an understanding of the invention, reference is made in thedescription to the accompanying drawings where the traffic informationsystem is illustrated in a preferred embodiment It is to be understoodthat the traffic information system of the present invention is notlimited to the preferred embodiment as illustrated in the drawings. Inthe drawings:

[0053]FIG. 1 is a schematic diagram of an embodiment of a trafficinformation system in accordance with the present invention;

[0054]FIG. 2 is a flow diagram illustrating an embodiment of the trafficforecasting process in accordance with the present invention;

[0055]FIG. 3 is a diagrammatic representation of a typical trafficintersection identifying travel links and individual traffic flows;

[0056]FIG. 4 is a diagrammatic representation of a typical relationshipbetween vehicle flow and vehicle concentration for a typical tic link;

[0057]FIG. 5 is a diagrammatic representation of typical relationshipsbetween vehicle flow rate and vehicle concentration for variousdifferent classes of roads; and

[0058]FIG. 6 is a diagrammatic representation of the relationshipbetween mean free vehicle speed and degree of saturation as derived inan embodiment of the invention.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT OF THE INVENTION

[0059] A preferred embodiment of the invention is described below withreference to the accompany FIGS. 1 to 6.

[0060] Real Time Traffic Related Data

[0061] Referring now to FIG. 1, there is shown a schematic diagram ofone embodiment of a traffic information system 1 of the presentinvention In the instance of the preferred embodiment, the systemincludes the means for sending customised traffic information directlyto travellers who have subscribed to receive such information. Ofcourse, the system could provide the customised information to a thirdpat information distributor who in turn effects the distribution of thecustomised information to individual subscribers.

[0062] The system of FIG. 1 includes various sources 2 for providingreal time traffic related data and associated data. These sources 2 mayinclude publicly available data, private data or proprietary data. Adatabase 3 stores historical traffic data The database 3 interfaces withsources 2 to receive real time traffic data and associated data. Thedatabase 3 also includes means for integrating historical, real time andassociated traffic data including means to perform a statisticalanalysis of the data, and is adapted to produce customised travellerinformation packages.

[0063] A member or subscriber database 4 stores travel profiles forindividual subscribers which are consulted when producing travellerinformation packages for individual subscribers 7. An SMS server 5provides a gateway between the database 3 and a wireless network 6, suchas a mobile or GSM network.

[0064] The subscriber 7 has mobile communications means, such as amobile phone which is operable to receive data transmitted via themobile network 6. In the preferred embodiment, customised text messagesare received on the subscriber's phone regarding relevant trafficinformation according to the subscriber's travel profile. The subscriber7 maybe provided with access to his profile stored in the subscriberdatabase 4, via the Internet or other access means. This provides thesubscriber 7 with the opportunity to edit and alter his travel profile.

[0065] The various sources 2 of data generally provide real time trafficrelated data. Such sources can include highway loop detectors, videocameras, publicly and privately owned sources, and vehicles fitted withGPS devices having radio or mobile communication devices fortransmitting data relating to the progress of the vehicle through thetraffic network Additionally, air surveillance may be used as well asgeneral media reports.

[0066] The interface between the database 3 and the sources 2 of datainclude automatically collected public information from various websites such as weather forecasts and other visual and voice informationwhich are received and keyed in by operators to add to the automaticallycollected information.

[0067] Integrating Historical, Real Time and Associated Traffic Data

[0068] The database 3 incorporates software to integrate and processhistorical traffic data and any real time data with respect to asubscriber's travel profile. Numerous techniques are available for suchprocessing. The software in the preferred embodiment is written in Perland in one embodiment, the software operates on a PC using the Linuxoperating system.

[0069] Referring to Table 1, there is shown an example of the type ofinformation stored in the database 3. TABLE 1 Entry Exit No. SuburbDepart Entry Time Exit Time Desin 1341 Box Hill 6.30 MDL 7.05 HOD 7.20BMDS 1342 Donvale 6.40 SPR 6.47 HOD 7.20 SUN 1343 N. Balwyn 6.40 BUL6.50 HOD 7.10 CBD 1345 Kew 6.40 BRI 6.50 HOD 7.20 CAR 1345 S. Wantirna6.45 SPR 7.00 HOD 7.30 CED 1346 S. Wantirna 6.45 SPR 7.00 HOD 7.30 CED1347 Wheelers Hill 6.50 WEL 6.55 PNT 7.35 BMDS 1348 Wheelers Hill 6.50SPR 6.55 HOD 7.50 BMDS 1349 E. Doncaster 6.50 DON 7.00 HOD 7.30 ABB 1350Balwyn 7.00 CHN 7.10 HOD 7.30 BMDS 1351 Glen Waverley 7.00 BILK 7.10 HOD7.45 CBD 2352 Rowville 7.00 WEL 7.10 PNT 8.00 BIRD 1353 Rowville 7.00WEL 7.10 PNT 8.10 BMDS 1354 E. Doncaster 7.00 BILK 7.12 HOD 8.00 BMDS1355 Rowville 7.00 WEL 7.15 PNT 8.20 CBD 1356 Greensborough 7.00 BRK7.20 HOD 7.50 CBD

[0070] In this instance, real time data has been integrated withhistorical data to produce predicted travel times for given routes. Theexample relates to travel from outer suburbs to the Monash freeway inMelbourne, Victoria. By way of illustration, the entry number 1345 willbe considered as an example. The information contained in this entryenables the system to predict that departing South Wantirna at 6.45 amwill mean entry onto the freeway at around 7.00 am via the Springvaleentrance. Exit at Hoddle St is forecasted to be at 7.30 am. Theinformation contained in the database 3 may be continually updated.

[0071] Referring again to FIG. 1, the SMS server 5 receives customisedmessages from the database 3 in the form of e-mails. The e-mails containthe customised forecasted traffic information for the individualsubscribers 7. The forecasted travel time information, incidents andweather information is delivered via the SMS server 5 over the mobilenetwork 6 to the mobile phones of the subscribers 7. The forecastedinformation can be delivered during various time windows such as thenight before, just prior to commencement of the journey, en-route orjust before bifurcation point offering the choice of alternative routesto the destination. Subscriber profiles contained in the subscriberdatabase 4 determine the frequency and time of the forecastedinformation being delivered.

[0072] Table 2 illustrates a snapshot of customised messages generatedand delivered to individual subscribers. TABLE 2 23/11/99 07:16:01:establish ppp link 23/11/99 07:16:03: sending Sathish, APPROX 22 mins TOPNT to 0414123456@trial.epus.com.au 23/11/99 07:16:04: sent 1 mailmessages 23/11/99 07:21:07: sending Grant, APPROX 16 mins TO HOD to0414234567@smsa.erics.com.au 23/11/99 07:25:30: Processing Eastern +Freeway 23/11/99 07:25:31: Processing Monash + Freeway 23/11/9907:25:31: Processing West + Gate + Freeway 23/11/99 07:26:12: sendingTracey, APPROX 13 mins TO HOD to 04191234561@cfsms@ericn.com.au 23/11/9907:31:17: sending Michael, SPR -> HOD 19 mins to0415987654@semes.epa.com.au 23/11/99 07:31:19: sent 3 mail messages23/11/99 07:38:23: sending George, JAC -> PNT 28 mins to041912345l@xyz.spanyb.com.au 23/11/99 07:41:28: sending Geoff, APPROX 15mins TO HOD to 0414333444@abc.eric.com.au 23/11/99 07:41:38: sent 2 mailmessages

[0073] For example, the message sent to Sathish at 7.16 am forecaststhat it will take him 22 minutes to reach Punt Rd. Similarly, for Grant,the message at 7.21 am forecasts that it will take him 16 minutes toreach Hoddle St. Typically these messages are sent to the subscribersprior to the commencement of their journey.

[0074] For example, the subscriber who leaves home from Rowville at 8.00am and enters the Monash freeway at Wellington Rd at approximately 8.15am would get a standard forecasted traffic information message at 7.55am. If an incident occurs between 7.55 am and 8.15 am, a further messageis sent via the mobile phone to that subscriber before he enters thefreeway. In this way, the subscriber is informed at critical times ofthe traffic situation on his route of travel thus enabling subscriber toalter their normal travel route in an attempt to avoid delays caused bythe incident.

[0075] Further down the table, there are three entries which show theprocessing of traffic information on the freeways. This illustrates theoperation of integrating real time data with historical data to provideforecasted information. The processing is conducted periodically or whenupdated real time data is received in the database.

[0076] Referring to FIG. 2, there is shown a process flow diagram of oneexample of the traffic forecasting process employing statisticalmodelling. The process flow includes the following steps:

[0077] Step 1: Obtain an accumulated series of historical data whichcould be in the form of continuous 2-10 minute averages of delay invarious geographical locations thus forming a series of historial dealysin time steps.

[0078] Step 2: Using conventional spectral methods, seasonal trends Inthe historical data are obtained and removed from the historical dataand the result output and tabled as Traffic data.

[0079] Step 3: Obtain historical weather data and splice it with theremaining unaltered traffic data so that for each entry in the trafficdata table, columns of a) Days since last rain (D_(r)); and b) Rainfallin last 3 hours (R) are added. Then add columns to the previously outputtraffic data table to indicate whether the data is in some or all of:

[0080] i) School holiday period (S_(h)=0 or 1)

[0081] ii) Common summer holiday period (C_(h)=0 or 1)

[0082] iii) Weekend/weekday (W_(e)=1/0)

[0083] iv) Public Holiday or day before/after (P_(h), P_(h) ⁻, P_(h) ⁺)

[0084] The above variables are examples of associated traffic data thatrelates to the types of events that can be modelled into the process andsimilarly, other variables may be entered as well. By adding theadditional columns described, the effect of weather patterns relating tothe various events and certain time periods are added to the Trafficdata table.

[0085] To generate a model of the historical data suitable forpredictive analysis, the traffic data is divided into seven filescorresponding to each day of the week. The data in each file is combinedby averaging which represents 15 minute or 30 minute averages dependingon what frequency is required. Typically, 30 minute periods will besufficient So in operation, for example, consider the average delay at8:30 am on a Monday morning. The representative delay data for aparticular or given route of travel comprises the individual delays forthe intersections and/or freeways, referred to as links, on that routeat the expected commencement time for each link of the route. Theaverage delay for each link for the 30 minute period between 8.30 am to9.00 am, is obtained from a time average of two minute intervals overthe 30 minute period. The sample interval of two minutes is a continuousstream of data obtained from sensors or the like at freeways,intersections, etc. The interval period maybe varied depending on thefrequency that is required.

[0086] All such average delay data gathered for 8:30 am on a Mondaymorning, are grouped into a separate file and a least squares fit of thedata is performed using the function:

Delay=a ₀ +a ₁ *D _(r)/(a ₂ +D _(r))+a ₃ *R/( a ₄ +R)+a ₅ *S _(h)+a ₀ *C_(h) +a ₇ *W _(e) +a ₈ *P _(h) +a ₉*P_(h) ⁻ +a ₁₀ *P _(h) ⁺

[0087] where:

[0088] D_(r): Days since last rain

[0089] R: Rainfall in last three hours

[0090] S_(h): School holiday period

[0091] C_(h): Common summer holiday period

[0092] W_(e): Weekend/weekday

[0093] P_(h): Public holiday

[0094] P_(h) ⁻: Day before public holiday

[0095] P_(h) ⁺: Day after public holiday

[0096] In the preferred embodiment, the above modelling is performed foreach 30 minute period of each day for seven days. This generates 336sets of the 10 coefficients (a₀ to a₉) which describe the historicaldata for each link Whilst not necessary, the use of a model to describethe historical data should result in a reduced primary and secondarystorage requirement as compared with storing all the available averagedhistorical data in RAM. Where a model is used, it is expected that themodel would be regenerated every six months or so. In the instance ofthe preferred embodiment, the least squares fit analysis would beexecuted every six months to generate a new 336 sets of the coefficients(a₀ to a₉) for each link.

[0097] Step 4: Obtain real time data from various sources relating tomeasured link delays of the network and associated data relating to theactual weather conditions for the links in the network.

[0098] Step 5: For each link in the network, determine the historicallyexpected delay based upon the seasonally adjusted historical delay andthe measured weather conditions and compute for each link the ratio ofthe most recently measured delay for the link to the historicallyexpected delay for the link at a time step corresponding to the measureddelay. This ratio is labelled “JVL”.

[0099] Step 6: When predicting the expected delay from a commencementnode to a destination node, determine the historically expected delayfor each link as it would be at the expected commencement time for eachlink and multiply the historically expected delay for each link of theroute by the link's corresponding JVL ratio prior to summing thehistorically expected delays on each of the links to thus form apredicted expected delay for travel from the commencement node to thedestination node.

[0100] Incidents that affect the expected travel delay on a traffic linkin a network are entered manually into a database by an operator. Due tothe wildly varying nature of incidents, the expected delay that willoccur to traffic on an affected link is necessarily reliant upon theestimation of a human observer. The observations of incident observersand the expected link delays resulting from incidents are entered into adatabase that the integrating means accesses on a regular basis toupdate the database of historically expected link delays. In anotherembodiment, the expected delays to traffic links caused by incidents mayremain in a separate database as compared with the database ofhistorically expected link dealys and the two databases may be accessedat the time of providing a predicted actual delay for a travellertravelling from a commencement node to a detination node in the network.Over time, as further observations are received regarding incidents, theincident database may be updated to reflect any change in the expecteddelay caused by the incident.

[0101] In the preferred embodiment, the incident database is accessedevery time a traveller profile causes the prediction and transmission ofthe travel delay for the subscriber.

[0102] Optimal Path Through Traffic Network

[0103] In a preferred embodiment of the invention, the customisedforecasted traffic information system includes the determination of anoptimal path through the traffic network for the subscriber to reach hisor her destination in the least time. The search for the optimal paththrough a traffic network takes account of each link flow direction andthe various different delays caused by traffic control signals totraffic movement through each intersection as well as operator input andother automatic data feeds.

[0104] With reference to FIG. 3, a diagrammatic representation of atraffic intersection 11 is provided with incoming/outgoing linksconnecting it to intersections (nodes) 12, 13, 14 and 15. The links maybe bi-directional and there may be more nodes connected to node 11 thandetailed in FIG. 3. In general, traffic arriving at B (or the queueterminating at B) will take different times to move through theintersection to C, D or E. These times will be dependent upon thecongestion on each of the links and the traffic signal settings atintersection 11.

[0105] For the purposes of this specification, the term “degree ofsaturation” (DOS) is used to refer to the ratio of the actual flow oftraffic movement resulting from a particular traffic signal as comparedwith the maximum possible flow of traffic resulting from that signal.DOS is a measure of the intersection congestion and may, under somecircumstances, be transformed to a delay in seconds for the particularmovement.

[0106] For the purposes of this specification, the term “mean freetravel time” is used to refer to the travel time down a link when alltraffic control devices are removed.

[0107] With reference to FIG. 4, a typical relationship between vehicleflow and vehicle concentration is detailed for a typical traffic link.As will be noted from FIG. 4, the relationship is a convex curveintersecting the vehicle concentration axis at a vehicle concentrationand saturation of zero.

[0108] A value for the “mean free speed” for the traffic link may bedetermined from FIG. 4 by dividing the vehicle flow (expressed asvehicles per second) by the corresponding concentration (expressed asvehicles per metre). As it will be recognised by those skilled in theart, the “mean free speed” is a difficult quantity to determine.

[0109] In a preferred embodiment of the invention, the mean free vehiclespeed down each link of a carriageway is obtained from the relationshipbetween vehicle flow rate and vehicle concentration for variousdifferent classes of roads (e.g. freeways, arterials, suburban streets).FIG. 4 also details the point at which saturation flow on a tic linkoccurs (x_(m), f_(m)).

[0110] Typical relationships are detailed in FIG. 5 for differentclasses of roads. It is also preferable to determine a furtherrelationship between vehicle concentration and degree of saturation.Since the DOS is directly proportional to vehicle flow, the vehicle flowmay be deduced from the DOS. For vehicle flows less than the saturationflow on a link (ie less than f_(m) in FIG. 4), the mean free speed maybecalculated by dividing the flow (deduced from the DOS) by thecorresponding concentration. For vehicle flows greater than thesaturation flow, corresponding to higher DOS, the vehicles experience arapid decrease in mean free speed to close to zero. The relationshipbetween mean free speed and DOS is shown in FIG. 6. The actualrelationship beyond the Degree of Saturation corresponding to saturationflow may be obtained from experiment. Accordingly, the DOS can be usedto estimate a flow rate which can be divided by the vehicleconcentration to provide the mean free speed.

[0111] A model of travel time from A to B to C to F is to sum theun-congested travel times from A to B and C to F with the delay in themovement B to C. The un-congested travel times are the mean free traveltimes. These times can be calculated from mean free travel speeds, whichare generally constant for all roads of a particular type, and the linklength. Computationally, it is convenient to define a link travel timeas the mean free travel time plus the time to negotiate the immediateupstream intersection. That is, the travel time on link BCF is the meanfree travel time on link CF plus the time to negotiate the movement BC.The latter may be computed from quantities transmitted by the trafficcontrol system at regular time intervals (eg 1 minute). For example, inthe SCATS (Sydney Co-ordinated Adaptive Traffic System) traffic controlsystem, the variables needed for the calculation of intersectionmovement delays are:

[0112] Date/Time

[0113] Intersection Strategic Approach number (e.g. a link number)

[0114] Regional Computer name/number (the identity of the computerproviding data)

[0115] Sub System number

[0116] Green signal time for the strategic approach

[0117] Signal cycle time for the strategic approach

[0118] DOS for the strategic approach

[0119] Defining link travel times as described above means thattraditional optimal path searching methods, for example Dijkstra, may beused. However, it also means that there are several travel timesassociated with each particular link. For example, the travel timesassociated with each link comprise the times for intersection movementsBC, EC, DC and CFC (a U turn) added to the mean free travel time downlink CF, that is, four link travel times.

[0120] Generally, if there are n links joined at a node, there are n*ntravel times associated with that node. When conducting a search from agiven node for the next node in the optimal path, the upstream node onthe path to the current node needs to be known, in order that thecorrect link travel time to the new node can be computed.

[0121] In the preferred embodiment of the invention, for each differenttime of day at which an optimal search is conducted, the link traveltimes are stored in a single continuous vector. Another vector includesindices of the first vector where information about the delays through aparticular node can be found.

[0122] For example, suppose NCOST is a vector of travel times andNINDEX(nn) is the index of intersection nn at which link travel timesstart in NCOST. If there are k links joining at intersection nn, thenthe link travel times for links joining node nn occupy positionsNINDEX(nn) to NINDEX(nn)+k*k−1 in vector NCOST. Specifically, ifi=(NINDEX(nn)+j*k−1+n) with j<k and n<=k, then NCOST(i) is the delaybetween node nn and the j'th downstream node given that traffic enterednode nn from upstream node n. This approach is a relatively efficientmethod of storing link delays which is updated easily as newintersection delays become available.

[0123] It is only necessary to store single connections between nodessince the travel time on CF given that arrival at node 1 was via ABoccupies a different position in the vector NCOST than the travel timeon BA given that arrival at node 1 was via FC. Bi-directional flow ishandled in this way.

[0124] In practice, NCOST may be two dimensional, the first dimensionreferring to the time of day and the second referring to link delays asdescribed above. For example, if the system is running on 10 minuteaverage data from traffic control signals, the first dimension will be144, as there are 144 separate 10 minute periods in a day.

[0125] Traffic incidents like road-works, temporary/permanent roadclosures and accidents can be handled in the above scheme by entering avery large link travel time in the appropriate position of NCOST for theknown or estimated time of the incident. In the case of uni-directionallinks, a very large link delay may be entered permanently in theposition of NCOST which relates to the illegal movement direction.

[0126] Most incidents will be handled by an operator typing codesrelating to the incidents into a file. The computer program will readthe file regularly (eg, every 5 seconds) and update NCOST. If anincident has occurred and the delays on appropriate links have been setto large values, the data coming from the traffic control system can becompared with historical data for the part of the network affected bythe incident. As the incident is cleared, the dynamic data will returnto “normal” and the large link delays can also be returned to normalvalues. The dynamic data thus provides a feedback path to the incidentdetecting operator.

[0127] Historical data from the traffic signals should be collected sothat there exists a complete 24 hour typical data set for each day ofthe week. Before collecting this data, the minimum sampling periodshould be determined (eg 10 minutes). At the start of a given day, therelevant data set should be loaded into NCOST.

[0128] As each day progresses, the system should collect the currentsignal data, process it in a form suitable to fill the relevant timeslot of NCOST and archive it for off-line modification of the historicaldatabase.

[0129] When a request for an optimal or fixed trip time is received, thelink delays collected from the last few periods of traffic signal datamay be compared with the corresponding historical data, and estimatesmay be made of each element of complete vectors of NCOST for thefollowing n time steps using various methods including:

[0130] Time series analyses

[0131] Exponential weighting

[0132] Direct multiplication of the historical values by the ratio ofthe currently available delay to the historical delay for each linkdelay.

[0133] Incidents are added and removed if necessary by an operatordynamically. Incident reports may be received by voice and the essentialinformation extracted electronically using voice recognition techniquesand transferred to the database of incident reports.

[0134] The preferred embodiment of the invention recognises that at thetime a trip starts, the link delays part way through the trip are notthe delays at that same start time. As the search method proceeds, theelapsed time to each node in the trip is computed and the vector ofNCOST appropriate for that particular time is used when computing thenext link delay in the trip. Clearly, in the absence or failure of adynamic data feed, historical data can be used, but as dynamic databecomes available, it can be used to modify the succeeding vectors ofhistorical data in NCOST to reflect current traffic conditions. From theknown origin and destination of the trip, an approximate estimate of thetrip travel time can be made using pessimistic mean travel speedsappropriate to the time of day. This is then used to estate the numberof time periods in NCOST over which a prediction must be made.

[0135] Although the preferred embodiment has been described in relationto the SCATS system, it is conceivable that the present invention may bereadily adapted to receive and utilise data that is collected byalternative traffic control systems such as the SCOOT (Split CycleOffset Optimisation Technique) system.

[0136] Predicting Traffic Network Movement Delays

[0137] Some traffic control systems are unable to provide a completelyupdated set of movement delays at each intersection in less than onehour. However, they can update perhaps ten per cent of the intersectionsof the traffic network in less than ten minutes. By carefully choosingthe intersections from which to collect traffic data, this data may bematched to the remaining intersections for which timely data is notavailable.

[0138] The intersections chosen for data collection must cover thegeometry and capacity range of the intersections for which timely datais not available. One measure of capacity is average DOS over a day.Historical data allows collection of appropriate data and calculation ofthis quantity for all intersections. Any two prospective matches shouldhave similar DOS. Similarly, if historical green signal times and signalcycle times are available, daily averages of these can also be used formatching pairs of intersections.

[0139] Any two matched intersections preferably have the same number ofintersecting links. For data matching purposes, the orientation ofintersections are preferably arranged such that the links closest topointing to the Central Business District are aligned. In addition, bothintersections should preferably be as close as possible to being thesame distance from the Central Business District.

[0140] All of the above matching criteria are available “off line”. Thatis, they can be applied to the system if only the network geometry andappropriate historical data are known. The more matching factors thatcan be applied, the more accurate the match between the intersectionswill be. In the preferred embodiment, the criteria of matching numbersof links, distance from the Central Business District and orientationswith respect to the Central Business District are always used. Oncematches are determined, collected intersection data maybe exported tomatching intersections, providing a full set of traffic data for anentire network.

[0141] With respect to the matching process, it is interesting to notethat errors in link delays tend to cancel out over trips that traverse alarge number of links.

[0142] If traffic data is available for a portion of one city but noneis available for another city that has similar traffic flowcharacteristics to the first, intersection matching using some of theattributes discussed above can be performed to estimate the traffic datain the city for which no traffic data exists. Local knowledge in thecity to be matched may allow classification of the intersections by“busyness” which may be equated to ranges of the average daily DOSvalues for the intersections in the city where data has already beencollected. It is then possible to estimate travel times throughout theday in the new city. This approach allows a travel time advisory systemto be established in any city for which traffic characteristics areknown to be similar to those in a city already operating such afacility. Over time, appropriate data may be collected in the “new” cityto improve the historical database. Ideally, this historical data needsto be supplemented with dynamic data from floating or seeded vehicles inorder to be able to provide genuine real time traveller information. Inany event, applying a traffic data matching process at least enables afirst estimate to be established for a city with no actual availabletraffic data.

[0143] Alternative Means for the Delivery of Traffic Information

[0144] Alternative means for delivering the messages may include text tovoice conversion. The forecasted traffic information can be convertedinto speech and transmitted either as a voice call or if not answered,then left as a voice message for subsequent retrieval. In another form,traffic information from the database can also be made available througha menu based interactive voice response (IVR) system. Furthermore, HTMLtext can be truncated to more basic WML text suitable for display on WAPand/or 3G mobile phones.

[0145] Referring again to FIG. 1, in an alternative arrangement,positional data for individual subscribers 7 can be determined andrelated to the server 5. A GPS, US or other suitable positioning systemcan be employed to determine the exact position and status of anindividual subscriber. If the subscriber alters his travel departuretime, his customised messages can be dynamically updated based on hiscurrent status as determined by his positional data. Subscribers canalso request specific information as required. The service invoked in anSMS protocol is known as “push” and “pull” messages. A push/pull serviceis where an SMS message is sent from a subscriber's phone requestingtraffic information (a pull) and an SMS message is sent (a push) inreturn with the required information. Subscribers may also access adedicated web-site which has information for the general public as wellas specific access for the subscribers. The subscribers can alter theirprofiles as they desire. The types of information contained within asubscriber profile may include the subscriber's expected time ofdeparture, pay and alternate routes with which the subscriber Isfamiliar and the subscriber's preference for weather forecastinformation.

[0146] Conclusion

[0147] The method and system of the present invention embodies manyadvantages and it will be appreciated by persons skilled in the art thatnumerous variations and/or modifications may be made to the invention asshown in the specific embodiments without depart from the spirit orscope of the invention as broadly described. The present embodimentsare, therefore, to be considered in all respects as illustrative and notrestrictive.

The claims defining the invention are as follows:
 1. A system forproviding traffic or related information comprising: a database storinghistorical traffic data and being operable to receive substantially realtime traffic data and associated data; means for deterministicallyintegrating the historical and real time traffic and associated datawith respect to at least one traveller profile to produce customisedforecasted traffic information with respect to the at least onetraveller profile; and means for sending the customised forecastedtraffic information to an intended recipient wherein the customisedforecasted traffic information comprises at least a predicted traveldelay for at least one travel route described in the at least onetraveller profile, wherein, where there is insufficient traffic data fora link of the travel route, the means for integrating is operable to useavailable data in respect of a further link in place of the insufficienttraffic data on said link of said travel route in order to provide thepredicted travel delay.
 2. A system according to claim 1 wherein each ofsaid travel route link and said further link include a trafficintersection, such that available traffic data in respect of the trafficintersection of said further link is used in place of insufficienttraffic data in respect of the traffic intersection of said travel routelink.
 3. A system according to claim 2 wherein matching the suitabilityof said further link to said travel route link takes account of one ormore of the following: a) geometry of said intersections; b) orientationof said intersections; c) the relative DOS of said intersections; d)historical daily averages for signal cycle times for said intersections;e) distance of said intersections from locations of relatively highpopulation density.
 4. A system according to any one of claims 1 to 3wherein the intended recipient receives the customised forecastedtraffic information for all traveller profiles and separates theinformation relating to each traveller profile for subsequenttransmission to individual travellers according to their profile.
 5. Asystem according to any one of the previous claims wherein at least oneindividual traveller, having a traveller profile, has a remote terminaloperable to receive transmitted customised forecasted trafficinformation.
 6. A system according to claim 5 wherein the remoteterminal is operable to transmit information to the database.
 7. Asystem according to claim 5 or claim 6 wherein the remote terminal is amobile phone.
 8. A system according to any one of the preceding claimswherein customised forecasted traffic information is transmitted to atraveller prior to the commencement of a traveller's journey.
 9. Asystem according to any one of the preceding claims wherein customisedforecasted traffic information is transmitted to a traveller during thetraveller's journey.
 10. A system according to any one of the precedingclaims including a traveller database for storing individual travellerprofiles, the traveller profiles including data identifying thetraveller and data relating to the traveller's usual travel routes andusual time of commencement on those routes.
 11. A system according toany one of the preceding claims wherein the traveller's profileindicates the times that the traveller prefers to receive customisedforecasted traffic information.
 12. A system according to either claim10 or claim 11 wherein individual travellers are provided access to thetraveller database and may alter data contained in the database relatingto their profile.
 13. A system according to any one of the precedingclaims wherein the means for integrating historical, real time andassociated traffic data is operable to: a) determine a time series ofaverage delays from historical data for links in a traffic network thetime series extending over a pre-determined period of time; b) receivehistorical weather data and correlate that weather data with thehistorical traffic data and generating an average historical delay forthe links during various weather conditions; c) receive real time datarelating to weather in the geographic region of the traffic network; d)estimate the actual link delays that will occur on each of the trafficlinks for each of the time series based upon the received data; and e)generate a prediction of the actual delay from a commencement node to adestination node of the traffic network by summing the respective linkdelays of the links along the travel route using the estimate for eachlink delay at the time the traveller is expected to commence travelalong those links.
 14. A system according to claim 13 wherein theintegrating means determines seasonal trends in the historical trafficdata relating to average link delays and removes those seasonal trendsfrom the historical traffic data.
 15. A system according to either claim13 or claim 14 wherein the integrating means estimates the actualtraffic link delay that will occur for a link some time in the future byreceiving real time traffic data relating to measured delays on trafficlinks and calculating the ratio of the most recently measured trafficlink delay to the average historical link delay for the correspondingtime step at which the measurement was taken and multiplying the averagehistorical link delay for the link at a future time step with thepreviously established ratio thus generating an estimate of the actuallink delay that will occur for the link at the time step in the future.16. A system according to claim 15 wherein the integrating meansgenerates an estimate of the actual traffic link delay that will occurfor a link some time in the future for all traffic links of the networkrelevant to traveller profiles.
 17. A system according to claim 16wherein the integrating means estimates travel time on a particular linkon the basis of prior links to said particular link that have beentraversed and taking into account the delay involved with traversingsaid prior links and the intersections traversed.
 18. A system accordingto any one of claims 13 to 17 wherein the integrating means determinespredictions of actual delay that will occur for travel routes accordingto traveller profiles at the times required by each respective travellerprofile.
 19. A system according to any one of claims 13 to 18 whereinthe integrating means receives data relating to events such as schoolholiday periods, summer holiday periods, public holidays and weekendsand correlate that data with the historical traffic data.
 20. A systemaccording to any one of claims 13 to 19 wherein the integrating meansincludes a model of that data generated by performing a least squaresfit analysis to determine an average historical traffic delay using thefunction: $\begin{matrix}{{Delay} = {a_{0} + {a_{1}*{{Dr}/\left( {a_{2} + {Dr}} \right)}} + {a_{3}*{R/\left( {a_{4} + R} \right)}} +}} \\{{{a_{5}*{Sh}} + {a_{6}*{Ch}} + {a_{7}*{We}} + {a_{8}*{Ph}} + {a_{9}*{Ph}^{-}} + {a_{10}*{Ph}^{+}}}}\end{matrix}$

Where Dr represents period since last rain R represents rainfall in lastpredetermined period Sh represents school holiday period Ch representscommon summer holiday period We represents a weekend or weekday Phrepresents a public holiday Ph⁻ represents a day before a public holidayPh⁺ represents a day after a public holiday
 21. A system according toany one of claims 13 to 20 wherein estimates of link delays caused byincidents are received and stored for subsequent access by theintegrating means for summing with the historically expected link delayswhen predicted travel delays are generated.
 22. A system according toany one of the preceding claims including a means for determining anoptimal path of travel through a traffic network, the means being: a)operable to determine link travel time for each traffic link in anetwork; and b) operable to implement a path searching method todetermine the optimal path between two nodes in the traffic network, theoptimal path being the series of connected traffic links between the twonodes resulting in the least expected delay.
 23. A system according toclaim 22 wherein the determination of a link travel time for a trafficlink in a network is obtained by summing the mean free travel time forthat link and the average time required to negotiate the immediateupstream intersection connected to that link.
 24. A system according toclaim 23 wherein the traffic links with vehicle flow rates less than thesaturation flow rate, the mean free travel speed is obtained by dividingthe vehicle flow rate for that link by the vehicle concentration forthat link, and the mean free travel time is obtained by dividing thedistance of the link by the mean free travel speed.
 25. A systemaccording to any one of claims 23 or 24 wherein the average time tonegotiate an immediate upstream intersection is obtained from thetraffic network's traffic control system.
 26. A system according to anyone of claims 22 to 25 wherein upon receiving a request to provide anoptimal travel path through a traffic network, measured link delays mostrecently collected from traffic signal data are compared with historicallink delay data and an estimate of the actual link delay data isgenerated for each traffic link in the network for an appropriate numberof steps in the time series according to the traveller's commencementand destination nodes in the network.
 27. A system according to claim 26where, in the even that real time link delay data for a traffic link isnot available, historical link delay data is used.
 28. A systemaccording to any one of the preceding claims where there is insufficienthistorical traffic data for links in a traffic network to generatehistorical link delay data; the means for integrating historical andreal time data uses available traffic data for other links in the sameand/or different network as estimates for those various links for whichthere is no data available.
 29. A system according to claim 28 whereinthe traffic links for which no traffic data is available are matched toother traffic links for which traffic data is available, the matchingprocess taking account of one or more of the following: a) the relativegeometry of the traffic links; b) the relative arrangement of thetraffic links; c) the relative capacity of the traffic links; d) therelative alignment of the traffic links with locations of relativelyhigh population density; e) the relative DOS of the traffic links; f)the relative distance of the traffic links from locations of relativelyhigh population density.
 30. A system according to any one of thepreceding claims wherein traffic data available for a portion of a cityor urban area is matched to another city or another urban area havingsimilar traffic flow characteristics to the first mentioned city orurban area and no available traffic data, in order to estimate thetraffic data in said another city or said another urban area.
 31. Amethod of providing traffic or related information comprising the stepsof: a) storing historical, real time and associated traffic data in adatabase; b) deterministically integrating said historical, real timeand associated data with respect to a traveller profile to producecustomised forecasted traffic information with respect to the travellerprofile; and c) sending the customised forecasted traffic information toan intended recipient wherein the customised forecasted trafficinformation comprises a predicted travel delay for a travel routedescribed in the traveller profile; wherein, where there is insufficienttraffic data for a link of the travel route, the step of integratingcomprises using available data in respect of a further link in place ofthe insufficient traffic data on said link of said travel route in orderto provide the predicted travel delay.
 32. A method according to claim31 further comprising the step of, where each of said travel route linkand said further link include a traffic intersection, using availabletraffic data in respect of the traffic intersection of said further linkin place of insufficient traffic data in respect of the trafficintersection of said travel route link.
 33. A method according to claim32 wherein said step of using available traffic data takes account ofone or more of the following: a) geometry of said intersections; b)orientation of said intersections; c) the relative DOS of saidintersections; d) historical daily averages for signal cycle times forsaid intersections; e) distance of said intersections from locations ofrelatively high population density.
 34. A method according to any one ofclaims 31 to 33 comprising the step of the intended recipient receivingthe customised forecasted traffic information for all traveller profilesand separating the information relating to each traveller profile andtransmitting the relevant traffic information to each individualtraveller according to their profile.
 35. A method according to any oneof claims 31 to 34 comprising the step of an individual traveller,having a traveller profile and a remote terminal, receiving customisedforecasted traffic information on that terminal.
 36. A method accordingto claim 35 wherein the remote terminal is operable to transmitinformation to the database, the method comprising the step of anindividual traveller transmitting information to the database.
 37. Amethod according to any one of claims 31 to 36 comprising the step ofdetermining from the traveller profiles the travellers' usual travelroutes and usual commencement time on those routes.
 38. A methodaccording to any one of claims 31 to 37 comprising the step ofdetermining from the traveller's profile the times that travellersprefer to receive customised forecasted traffic information.
 39. Amethod according to claim 38 comprising the step of sending customisedforecasted traffic information to a traveller in accordance with thepreferred times for receiving the information as determined from thetraveller's profile.
 40. A method according to either claim 38 or claim39 including the step of a traveller accessing their stored travelprofile and altering the data contained in that profile.
 41. A methodaccording to any one of claims 31 to 40 wherein the step of integratinghistorical, real time and associated data with respect to thetraveller's profiles to produce customised forecasted trafficinformation includes the steps of: a) determining the time series ofaverage delays from historical data for links in a traffic network, thetime series extending over a predetermined period of time; b) receivinghistorical weather data and correlating that weather data with thehistorical traffic data and generating an average historical delay forthe links during various weather conditions; c) receiving real time datarelating to weather in the geographic region of the traffic network; d)estimating the actual link delays that will occur on each of the trafficlinks for each of the time series based upon the received data; and e)generating a prediction of the actual delay from a commencement node toa destination node of the traffic network by summing the respective linkdelays of the links along the travel route using the estimate for eachlink delay at the time the traveller is expected to commence travelalong those links.
 42. A method according to claim 41 wherein the stepof integrating historical, real time and associated data with respect totraveller profiles includes the step of determining seasonal trends inthe historical data relating to average link delays and removing thoseseasonal trends from the historical traffic data.
 43. A method accordingto either claim 41 or claim 42 wherein the step of integratinghistorical, real time and associated data with respect to travellerprofiles includes the step of estimating the actual traffic link delaythat will occur for a link some time in the future by receiving realtime traffic data relating to measured delays on traffic links andcalculating the ratio of the most recently measured traffic link delayto the average historical link delay for the corresponding time step atwhich the measurement was taken and multiplying the average historicallink delay for the link at a future time step with the previouslyestablished ratio thus generating an estimate of the actual link delaythat will occur for the link at the time step in the future.
 44. Amethod according to claim 43 wherein the step of integrating historical,real time and associated data with respect to traveller profilesincludes the step of generating an estimate of the actual traffic linkdelay that will occur for a link some time in the future for all trafficlinks of the network relevant to traveller profiles.
 45. A methodaccording to any one of claims 41 to 44 wherein the step of integratinghistorical, real time and associated data with respect to travellerprofiles includes the step of determining predictions of actual delaysthat will occur for travel routes according to traveller profiles at thetimes required by each respective traveller profile.
 46. A methodaccording to any one of claims 41 to 45 wherein the step of integratinghistorical, real time and associated data with respect to travellerprofiles includes the step of receiving data relating to events such asschool holiday periods, summer holiday periods, public holidays andweekends and correlating that data with the historical traffic data. 47.A method according to any one of claims 41 to 46 wherein the step ofintegrating historical, real time and associated data with respect totraveller profiles includes the step of modelling the historical andreal time data by performing a least squares fit analysis to determinean average historical delay using the function: $\begin{matrix}{{Delay} = {a_{0} + {a_{1}*{{Dr}/\left( {a_{2} + {Dr}} \right)}} + {a_{3}*{R/\left( {a_{4} + R} \right)}} +}} \\{{{a_{5}*{Sh}} + {a_{6}*{Ch}} + {a_{7}*{We}} + {a_{8}*{Ph}} + {a_{9}*{Ph}^{-}} + {a_{10}*{Ph}^{+}}}}\end{matrix}$

Where Dr represents period since last rain R represents rainfall in lastpredetermined period Sh represents school holiday period Ch representscommon summer holiday period We represents a weekend or weekday Phrepresents a public holiday Ph⁻ represents a day before a public holidayPh⁺ represents a day after a public holiday
 48. A method according toany one of claims 41 to 47 wherein the step of integrating historical,real time and associated data with respect to traveller profilesincludes the step of receiving data relating to estimated link delayscaused by incidents and storing that data for subsequent access wherebyestimated delays caused by incidents are summed with historicallyexpected link delays when predicted travel delays are generated.
 49. Amethod according to any one of claims 41 to 48 further comprising thestep of estimating travel time on a particular link on the basis ofprior links to said particular link traversed and taking account of thedelay involved with traversing said prior links and intersections.
 50. Amethod according to any one of claims 41 to 49 including the step ofidentifying an optimal path of travel through a traffic network bydetermining a link travel time for each traffic link in a network andimplementing a path searching method to determine the optimal pathbetween two nodes in the traffic network, the optimal path being theseries of connected traffic links between the two nodes resulting in theleast expected delay.
 51. A method according to claim 39 wherein thestep of determining link travel time for a traffic link in a networkincludes the step of summing the mean free travel time for that link andthe average time required to negotiate the immediate upstreamintersection connected to that link.
 52. A method according to claim 51wherein the determination of the mean free travel time for a trafficlink includes the step of obtaining the mean free travel speed bydividing the vehicle flow rate for the link by the vehicle concentrationfor the link and obtaining the mean free travel time by dividing thedistance of the link by the mean free travel speed.
 53. A methodaccording to either claim 51 or claim 52 including the step of obtainingan average time to negotiate an immediate upstream intersection from thetraffic network traffic control system.
 54. A method according to anyone of claims 50 to 53 including the step of identifying a request toprovide an optimal travel path through a travel network and upon receiptof such a request, comparing measured link delays most recentlycollected from traffic signal data with historical link delay data andestimating the actual link delay for each traffic link in the networkfor an appropriate number of steps in the time series according thetraveller's commencement and destination nodes in the network.
 55. Amethod according to claim 54 including the step of identifying whethersufficient recent real time link delay data for a traffic link isavailable according to a pre-established criteria and in the event thatsufficiently recent link time delay data is not available, making use ofhistorical link delay data.
 56. A method according to any one of claims41 to 55 including the step of identifying whether there is insufficienthistorical traffic data for links in a traffic network to generatehistorical link delay data in accordance with a pre-established criteriaand in the event of identifying that insufficient data exists, usingavailable traffic data for other links in the same and/or differentnetworks as estimates for those various links for which there is noavailable data.
 57. A method according to claim 56 wherein the step ofusing available traffic data for other links in the same and/ordifferent network as estimates for the various links for which there isno available data includes the step of matching the traffic links forwhich no traffic data is available to other traffic links for whichtraffic data is available, the method step taking account of one or moreof the following: a) the relevant geometry of the traffic links; b) therelative arrangement of the traffic links; c) the relative capacity ofthe traffic links; d) the relative alignment of the traffic links withlocations of relatively high population density. e) the relative DOS ofthe traffic links; f) the relative distance of the traffic links fromlocations of relatively high population density.
 58. A method accordingto any one of claims 31 to 57 further comprising the step of matchingtraffic data available for a portion of a city or urban area to anothercity or another urban area having similar traffic flow characteristicsto the first mentioned city or urban area, in order to estimate thetraffic data in said another city or said another urban area, wherein notraffic data is available for said another city or said another urbanarea.